Visualizing Crop Disease Detection Exploring Deep Learning with Custom CNN Model and XAI for Enhanced Interpretability

Accurately detecting plant diseases is essential for safeguarding food security and promoting agricultural sustainability. This study presents an advanced deep learning approach using Convolutional Neural Networks (CNNs) to classify plant leaf diseases. Utilizing the Plant Village dataset, a custom CNN model was developed, which achieved superior accuracy of $\mathbf{9 6. 1 1 \%}$, surpassing popular transfer learning models such as ResNet152-v2 (94%), VGG16 (91%), and Inceptionv 3 ($88 \%$). Key aspects of this work include the implementation of robust data preprocessing techniques like image augmentation and the design of an optimized CNN architecture to enhance performance while maintaining computational efficiency. Furthermore, explainability was explored using Layer CAM, which provided more detailed visual explanations than Grad-CAM, thereby increasing the model’s transparency and trustworthiness. These results confirm the effectiveness of the proposed method for accurate plant disease identification, positioning it as a promising tool for agricultural disease diagnosis. Future efforts will focus on expanding the dataset, improving model generalization, and deploying the model for real-time field applications.